Recruit firefighters: personality and mental health
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the predispositional hypothesis related to the “rescue personality” and the mental health of firefighter recruits. Design/methodology/approach This study compared responses to a written set of personality and mental health measures between firefighter recruits and non-rescue comparison participants – individually matched based on age, gender, ethnicity, and marital status. Data analysis involved statistical one-way between subjects analyses of variance complemented with epidemiological paired odds ratio calculations. Findings The results indicated that firefighter recruits self-reported as less open to experience, less neurotic, and less Type A. They also self-reported as less likely to report somatization, hostility, and posttraumatic stress symptomatology than comparison participants. Recruits were higher in extraversion and conscientiousness, but indicated no differences in perceptions of risk or sensation-seeking behaviour. Originality/value The present study contributes to the literature on firefighter recruits and provides some initial data regarding personality of those attracted to the fire services, as well as information about the mental health of firefighters prior to service. Mitchell’s “rescue personality” was partly supported and evidence was provided suggesting that new recruits have strong self-perceived mental health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".